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      Lytic bacteriophages induce the secretion of antiviral and proinflammatory cytokines from human respiratory epithelial cells

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          Abstract

          Phage therapy is a therapeutic approach to treat multidrug-resistant (MDR) infections that employs lytic bacteriophages (phages) to eliminate bacteria. Despite the abundant evidence for its success as an antimicrobial in Eastern Europe, there is scarce data regarding its effects on the human host. Here, we aimed to understand how lytic phages interact with cells of the airway epithelium, the tissue site that is colonized by bacterial biofilms in numerous chronic respiratory disorders. Using a panel of Pseudomonas aeruginosa phages and human airway epithelial cells (AECs) derived from a person with cystic fibrosis (CF), we determined that interactions between phages and epithelial cells depend on specific phage properties as well as physiochemical features of the microenvironment. Although poor at internalizing phages, the airway epithelium responds to phage exposure by changing its transcriptional profile and secreting antiviral and proinflammatory cytokines that correlate with specific phage families. Overall, our findings indicate that mammalian responses to phages are heterogenous and could potentially alter the way that respiratory local defenses aid in bacterial clearance during phage therapy. Thus, besides phage receptor specificity in a particular bacterial isolate, the criteria to select lytic phages for therapy should be expanded to include mammalian cell responses.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
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                Journal
                PLOS Biology
                PLoS Biol
                Public Library of Science (PLoS)
                1545-7885
                April 23 2024
                April 23 2024
                : 22
                : 4
                : e3002566
                Article
                10.1371/journal.pbio.3002566
                4eb182c7-3380-4bc5-9990-1f93c22c0fa4
                © 2024

                http://creativecommons.org/licenses/by/4.0/

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